Big Data Generation for Time Dependent Processes: The Tennessee Eastman Process for Generating Large Quantities of Process Data

Andersen, Emil; Udugama, Isuru A.; Gernaey, Krist V.; Bayer, Christoph; Külahçı, Murat

doi:10.1016/b978-0-12-823377-1.50219-6

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…The Tennessee Eastman process is a chemical process that was published for use a research benchmark for plant-wide control design (Downs and Vogel, 1993). In the two decades since it was published, it has been used by many workers as a basis for work on plant control (Lyman and Georgakis (1995); Rodriguez and Marcos (2002); Vinson et al (1995)), optimization (Jha and Okorafor (2014); Jockenhövel et al (2003)), simulation (Cameron and Gani (2011); Martin-Villalba et al ( 2018)), monitoring (Chen et al (2014); Jiang and Yan (2015); Lau et al (2013)), education (Udugama et al, 2020) and generation of synthetic datasets (Andersen et al (2020); Capaci et al (2019)). We will use the process to demonstrate how our system modelling can be used to structure design decisions and information about the process.…”

Section: Description Of the Processmentioning

confidence: 99%

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

Cameron

Waaler

Fjøsna³

et al. 2022

Front. Manuf. Technol.

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The on-going twin transition demands that the continuous process industry builds and operates their facilities in a more sustainable way. This change affects the entire supply-chain. The market demands new ways of engineering, procuring and constructing plants that assure quality at each step of the process. Petroleum and petrochemical producers must reduce their waste and environmental footprint and find ways of migrating to sustainable production. There is zero tolerance for waste, emissions or process malfunctions. Engineering contractors need to transfer their skills to new processes and produce series, non-custom facilities for new applications like offshore wind energy, modular production and industrial symbiosis. This is leading to a convergence in methods with discrete manufacturing, especially the automotive industries. In this climate, this sector can benefit from applying Zero-defect Manufacturing (ZDM) to both engineering design and operations. This work defines a framework for implementing ZDM in the process industry supply chain. The framework brings together modelling techniques and models from the following disciplines: system engineering, computer-aided process engineering, automation (especially Industry 4.0) and semantic technologies. These contributions are synthesised into an information fabric that allows engineering firms to work in new ways. Operators and contractors can use the fabric to move from document-driven engineering to data-based processes. The fabric captures requirements and intent in design so that facilities can be delivered and started-up and operated with zero defects in the design and construction. The information is also a vital support for safe and efficient operations and maintenance. We call this zero-defect O&M. The framework combines a systems engineering break-down of facilities, based on ISO/IEC81346, with implementation in SysML, with semantic interoperability frameworks from the process industries (ISO15926). We build upon and synthesise the results of recent standardization initiatives from the industry, notably CFIHOS, DEXPI and READI. We draw on results from process systems engineering, the OntoCAPE ontology and the CAPE-OPEN standards. The framework is illustrated by application to a non-proprietary process system, namely the Tennessee-Eastman process. This example is used to show the modelling approach and indicate how the fabric supports zero-defect practices.

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Section: Description Of the Processmentioning

confidence: 99%

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

Cameron

Waaler

Fjøsna³

et al. 2022

Front. Manuf. Technol.

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“…There exist 28 different types of faults. In order to generate datasets, we will use the generator presented in [8].…”

Section: The Tep Datasetmentioning

confidence: 99%

Hierarchical Multi-Fault Prognostics for Complex Systems

Pastor¹,

Nowaczyk²,

Pashami³

2022

SSRN Journal

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“…A few dynamic process modelling endeavours can successfully fulfil the above three criteria to be identified as validated models. Operator training simulators (OTS) are a great example where a dynamic process model is coupled with a graphical user interface (GUI) to train process operators on handling day-to-day or special operational situations as described in several publications [84][85][86][87][88]. Furthermore, Fig.…”

Section: The Number Of States (Internal Terms the Model Keepsmentioning

confidence: 99%

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

Udugama

López

Gargalo

et al. 2021

Syst Microbiol and Biomanuf

Self Cite

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The domain of industrial biomanufacturing is enthusiastically embracing the concept of Digital Twin, owing to its promises of increased process efficiency and resource utilisation. However, Digital Twin in biomanufacturing is not yet clearly defined and this sector of the industry is falling behind the others in terms of its implementation. On the other hand, some of the benefits of Digital Twin seem to overlap with the more established practices of process control and optimization, and the term is vaguely used in different scenarios. In an attempt to clarify this issue, we investigate this overlap for the specific case of fermentation operation, a central step in many biomanufacturing processes. Based on this investigation, a framework built upon a five-step pathway starting from a basic steady-state process model is proposed to develop a fully-fledged Digital Twin. For demonstration purposes, the framework is applied to a bench-scale second-generation ethanol fermentation process as a case study. It is proposed that the success or failure of a fully-fledged Digital Twin implementation is determined by key factors that comprise the role of modelling, human operator actions, and other propositions of economic value.

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Big Data Generation for Time Dependent Processes: The Tennessee Eastman Process for Generating Large Quantities of Process Data

Cited by 7 publications

References 7 publications

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

Hierarchical Multi-Fault Prognostics for Complex Systems

Digital Twin in biomanufacturing: challenges and opportunities towards its implementation

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